SoroushVahidi/scidocs huggingface dataset:
Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks
(2018)

Choosing optimal (or at least better) policies is an important problem in domains from medicine to education to finance and many others. One approach to this problem is through controlled experiments/trials but controlled experiments are expensive. Hence it is important to choose the best policies on the basis of observational data. This presents two difficult challenges: (i) missing counterfactuals, and (ii) selection bias. This paper presents theoretical bounds on estimation errors of counterfactuals from observational data by making connections to domain adaptation theory. It also presents a principled way of choosing optimal policies using domain adversarial neural networks. This illustrates the effectiveness of domain adversarial training together with various features of our algorithm on a semi-synthetic breast cancer dataset.
Examining the psychometric properties of a sport-related concussion survey: a Rasch measurement approach
(2017)

Awareness of sport-related concussion (SRC) is an essential step in increasing the number of athletes or parents who report on SRC. This awareness is important, as there is no established data on medical care at youth-level sports and may be limited to individuals with only first aid training. In this circumstance, aside from the coach, it is the players and their parents who need to be aware of possible signs and symptoms. The aim of this study was to examine the psychometric properties of a parent and player concussion survey intended for use before and after an education campaign regarding SRC. 1441 questionnaires were received from parents and 284 questionnaires from players. The responses to the sixteen-item section of the questionnaire’s ‘recognition of signs and symptoms’ were submitted to psychometric analysis using the dichotomous and polytomous Rasch model via the Rasch Unidimensional Measurement Model software RUMM2030. The Rasch model of Modern Test Theory can be considered a refinement of, or advance on, traditional analyses of an instrument’s psychometric properties. The main finding is that these sixteen items measure two factors: items that are symptoms of concussion and items that are not symptoms of concussion. Parents and athletes were able to identify most or all of the symptoms, but were not as good at distinguishing symptoms that are not symptoms of concussion. Analyzing these responses revealed differential item functioning for parents and athletes on non-symptom items. When the DIF was resolved a significant difference was found between parents and athletes. The main finding is that the items measure two ‘dimensions’ in concussion symptom recognition. The first dimension consists of those items that are symptoms of concussion and the second dimension of those items that are not symptoms of concussion. Parents and players were able to identify most or all of the symptoms of concussion, so one would not expect to pick up any positive change on these items after an education campaign. Parents and players were not as good at distinguishing symptoms that are not symptoms of concussion. It is on these items that one may possibly expect improvement to manifest, so to evaluate the effectiveness of an education campaign it would pay to look for improvement in distinguishing symptoms that are not symptoms of concussion.
Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging.
(1999)

A method for motion correction, involving both data collection and reconstruction, is presented. The PROPELLER MRI method collects data in concentric rectangular strips rotated about the k-space origin. The central region of k-space is sampled for every strip, which (a) allows one to correct spatial inconsistencies in position, rotation, and phase between strips, (b) allows one to reject data based on a correlation measure indicating through-plane motion, and (c) further decreases motion artifacts through an averaging effect for low spatial frequencies. Results are shown in which PROPELLER MRI is used to correct for bulk motion in head images and respiratory motion in nongated cardiac images. Magn Reson Med 42:963-969, 1999.
An efficient audio fingerprint search algorithm for music retrieval
(2013)

The conventional audio fingerprinting system by Haitsma uses a lookup table to identify the candidate songs in the database, which contains the sub-fingerprints of songs, and searches the candidates to find a song whose bit error rate is the lowest. However, this approach has a drawback that the number of database accesses increases dramatically, especially when the database contains a large number of songs or when a matching sub-fingerprint is not found in the lookup table due to a heavily degraded input signal. In this paper, a novel search method is proposed to overcome these difficulties. The proposed method partitions each song found from the lookup table into blocks, assigns a weight to each block, and uses the weight as a search priority to speed up the search process while reducing the number of database accesses. Various results from our experiment show the significant improvement in search speed while maintaining the search accuracy comparable to the conventional method.
Expression and export: recombinant protein production systems for Aspergillus
(2010)

Several Aspergillus species, in particular Aspergillus niger and Aspergillus oryzae, are widely used as protein production hosts in various biotechnological applications. In order to improve the expression and secretion of recombinant proteins in these filamentous fungi, several novel genetic engineering strategies have been developed in recent years. This review describes state-of-the-art genetic manipulation technologies used for strain improvement, as well as recent advances in designing the most appropriate engineering strategy for a particular protein production process. Furthermore, current developments in identifying bottlenecks in the protein production and secretion pathways are described and novel approaches to overcome these limitations are introduced. An appropriate combination of expression vectors and optimized host strains will provide cell factories customized for each production process and expand the great potential of Aspergilli as biotechnology workhorses to more complex multi-step industrial applications.
Analysis of Sentence Scoring Methods for Extractive Automatic Text Summarization
(2014)

Automatic text summarization is a major area of research in the domain of information systems. Most of the methods requires domain knowledge in order to produce a coherent and meaningful summary. In Extractive text summarization, sentences are scored on some features. A large number of feature based scoring methods have been proposed for extractive automatic text summarization by researchers. This paper reviews features for sentence scoring. The results on combinations of various features for scoring are discussed. ROUGE-N is used to evaluate generated summary with abstractive summary of DUC 2002 dataset.
Nonisolated High Gain DC–DC Converter for DC Microgrids
(2018)

DC microgrids are popular due to the integration of renewable energy sources such as solar photovoltaics and fuel cells. Owing to the low output voltage of these dc power generators, high efficient high gain dc–dc converters are in need to connect the dc microgrid. In this paper, a nonisolated high gain dc–dc converter is proposed without using the voltage multiplier cell and/or hybrid switched-capacitor technique. The proposed topology utilizes two nonisolated inductors that are connected in series/parallel during discharging/charging mode. The operation of switches with two different duty ratios is the main advantage of the converter to achieve high voltage gain without using extreme duty ratio. The steady-state analysis of the proposed converter using two different duty ratios is discussed in detail. In addition, a 100 W, 20/200 V prototype circuit of the high gain dc–dc converter is developed, and the performance is validated using experimental results.
SMOTE: Synthetic Minority Over-sampling Technique
(2002)

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of “normal” examples with only a small percentage of “abnormal” or “interesting” examples. It is also the case that the cost of misclassifying an abnormal (interesting) example as a normal example is often much higher than the cost of the reverse error. Under-sampling of the majority (normal) class has been proposed as a good means of increasing the sensitivity of a classifier to the minority class. This paper shows that a combination of our method of over-sampling the minority (abnormal) class and under-sampling the majority (normal) class can achieve better classifier performance (in ROC space) than only under-sampling the majority class. This paper also shows that a combination of our method of over-sampling the minority class and under-sampling the majority class can achieve better classifier performance (in ROC space) than varying the loss ratios in Ripper or class priors in Naive Bayes. Our method of over-sampling the minority class involves creating synthetic minority class examples. Experiments are performed using C4.5, Ripper and a Naive Bayes classifier. The method is evaluated using the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy.
An insight into imbalanced Big Data classification: outcomes and challenges
(2017)

BigData applications are emerging during the last years, and researchers frommany disciplines are aware of the high advantages related to the knowledge extraction from this type of problem. However, traditional learning approaches cannot be directly applied due to scalability issues. To overcome this issue, the MapReduce framework has arisen as a “de facto” solution. Basically, it carries out a “divide-andconquer” distributed procedure in a fault-tolerant way to adapt for commodity hardware.Being still a recent discipline, few research has been conducted on imbalanced classification for Big Data. The reasons behind this are mainly the difficulties in adapting standard techniques to the MapReduce programming style. Additionally, inner problems of imbalanced data, namely lack of data and small disjuncts, are accentuated during the data partitioning to fit theMapReduce programming style. This paper is designed under three main pillars. First, to present the first outcomes for imbalanced classification in Big Data problems, introducing the current B Alberto Fernández alberto@decsai.ugr.es Sara del Río srio@decsai.ugr.es Nitesh V. Chawla nchawla@nd.edu Francisco Herrera herrera@decsai.ugr.es 1 Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain 2 Department of Computer Science and Engineering, 384 Fitzpatrick Hall, University of Notre Dame, Notre Dame, IN 46556, USA 3 Interdisciplinary Center for Network Science and Applications, 384 Nieuwland Hall of Science, University of Notre Dame, Notre Dame, IN 46556, USA research state of this area. Second, to analyze the behavior of standard pre-processing techniques in this particular framework. Finally, taking into account the experimental results obtained throughout this work, we will carry out a discussion on the challenges and future directions for the topic.
The implementation of a full EMV smartcard for a point-of-sale transaction
(2012)

This paper examines the changes in the payment card environment as they relate to EMV (named after Europay, MasterCard and Visa). This research shows that if the combined dynamic data authentication (CDA) card variant of the EMV card is deployed in a full EMV environment, given the relevant known vulnerabilities and attacks against the EMV technology, the consequences of unauthorized disclosure of the cardholder data is of significantly reduced value to a criminal.
Challenges and chances for smart rail mobility at mmWave and THz bands from the channels viewpoint
(2017)

In the new era of “smart rail mobility”, infrastructure, trains, and travelers will be interconnected to achieve optimized mobility, higher safety, and lower costs. In order to realize a seamless high-data rate wireless connectivity, up to dozens of GHz bandwidth is required, and this motivates the exploration of the underutilized millimeter wave (mmWave) as well as the largely unexplored Terahertz (THz) bands. In order to realize the smart rail mobility at mmWave and THz bands, it is critical to gain a thorough understanding of the wireless channels. In this paper, according to the state of the art in research on railway wireless channels, we identify the main technical challenges and the corresponding chances concerning the reference scenario modules, accurate and efficient simulation platform, beamforming strategies, and handover design.
News via Voldemort: Parody accounts in topical discussions on Twitter
(2016)

Parody accounts are prevalent on Twitter, offering irreverent interpretations of public figures, fictional characters, and more. These accounts post comments framed within the context of their fictional universes or stereotypes of their subjects, responding in-character to topical events. This article positions parody accounts as a ritualised social media practice, an extension of fan practices and irreverent internet culture. By providing a typology of parody accounts and analysing the topicality of selected parody accounts’ tweets, the research examines how these accounts contribute to topical discussions. In-character framing of topical comments allows parody accounts to offer original interpretations of breaking news
Agglomerative Information Bottleneck
(1999)

We introduce a novel distributional clustering algorithm that explicitly maximizes the mutual information per cluster between the data and given categories. This algorithm can be considered as a bottom up hard version of the recently introduced “Information Bottleneck Method”. We relate the mutual information between clusters and categories to the Bayesian classification error, which provides another motivation for using the obtained clusters as features. The algorithm is compared with the top-down soft version of the information bottleneck method and a relationship between the hard and soft results is established. We demonstrate the algorithm on the 20 Newsgroups data set. For a subset of two news-groups we achieve compression by 3 orders of magnitudes loosing only 10% of the original mutual information.
DEVELOPMENT OF SEISMIC VULNERABILITY ASSESSMENT METHODOLOGIES OVER THE PAST 30 YEARS
(2007)

Models capable of estimating losses in future earthquakes are of fundamental importance for emergency planners and for the insurance and reinsurance industries. One of the main ingredients in a loss model is an accurate, transparent and conceptually sound algorithm to assess the seismic vulnerability of the building stock and indeed many tools and methodologies have been proposed over the past 30 years for this purpose. This paper takes a look at some of the most significant contributions in the field of vulnerability assessment and identifies the key advantages and disadvantages of these procedures in order to distinguish the main characteristics of an ideal methodology.
WiFi-SLAM Using Gaussian Process Latent Variable Models
(2007)

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.
Sentential Representations in Distributional Semantics
(2016)

This thesis is about the problem of representing sentential meaning in distributional semantics. Distributional semantics obtains the meanings of words through their usage, based on the hypothesis that words occurring in similar contexts will have similar meanings. In this framework, words are modeled as distributions over contexts and are represented as vectors in high dimensional space. Compositional distributional semantics attempts to extend this approach to higher linguistics structures. Some basic composition models proposed in literature to obtain the meaning of phrases or possibly sentences show promising results in modeling simple phrases. The goal of the thesis is to further extend these composition models to obtain sentence meaning representations. The thesis puts more focus on unsupervised methods which make use of the context of phrases and sentences to optimize the parameters of a model. Three different methods are presented. The first model is the PLF model, a practical composition and linguistically motivated model which is based on the lexical function model introduced by Baroni and Zamparelli (2010) and Coecke et al. (2010). The second model is the Chunk-based Smoothed Tree Kernels (CSTKs) model, extending Smoothed Tree Kernels (Mehdad et al., 2010) by utilizing vector representations of chunks. The final model is the C-PHRASE model, a neural network-based approach, which jointly optimizes the vector representations of words and phrases using a context predicting objective. The thesis makes three principal contributions to the field of compositional distributional semantics. The first is to propose a general framework to estimate the parameters and evaluate the basic composition models. This provides a fair way to comparing the models using a set of phrasal datasets. The second is to extend these basic models to the sentence level, using syntactic information to build up the sentence vectors. The third contribution is to evaluate all the proposed models, showing that they perform on par with or outperform competing models presented in the literature. Thesis Supervisor: Marco Baroni Title: Associate Professor
An Automatic Collocation Extraction from Arabic Corpus
(2011)

Problem statement: The identification of collocations is very importa n part in natural language processing applications that require some degree of semantic interpretation such as, machine translation, information retrieval and text summari z tion. Because of the complexities of Arabic, the collocations undergo some variations such as, morph ological, graphical, syntactic variation that constitutes the difficulties of identifying the col location. Approach: We used the hybrid method for extracting the collocations from Arabic corpus that is based on linguistic information and association measures. Results: This method extracted the bi-gram candidates of Ar abic collocation from corpus and evaluated the association measures by using the n-best evaluation method. We reported the precision values for each association measure in ea ch n-best list. Conclusion: The experimental results showed that the log-likelihood ratio is the best as ociation measure that achieved highest precision.
Burnout, compassion fatigue, compassion satisfaction, and secondary traumatic stress in trauma nurses.
(2014)

The relationship of burnout (BO), compassion fatigue (CF), compassion satisfaction (CS), and secondary traumatic stress (STS) to personal/environmental characteristics, coping mechanisms, and exposure to traumatic events was explored in 128 trauma nurses. Of this sample, 35.9% had scores consistent with BO, 27.3% reported CF, 7% reported STS, and 78.9% had high CS scores. High BO and high CF scores predicted STS. Common characteristics correlating with BO, CF, and STS were negative coworker relationships, use of medicinals, and higher number of hours worked per shift. High CS correlated with greater strength of supports, higher participation in exercise, use of meditation, and positive coworker relationships. Caring for trauma patients may lead to BO, CF, and STS; identifying predictors of these can inform the development of interventions to mitigate or minimize BO, CF, and STS in trauma nurses.
A Hierarchical Deep Architecture and Mini-batch Selection Method for Joint Traffic Sign and Light Detection
(2018)

Traffic light and sign detectors on autonomous cars are integral for road scene perception. The literature is abundant with deep learning networks that detect either lights or signs, not both, which makes them unsuitable for real-life deployment due to the limited graphics processing unit (GPU) memory and power available on embedded systems. The root cause of this issue is that no public dataset contains both traffic light and sign labels, which leads to difficulties in developing a joint detection framework. We present a deep hierarchical architecture in conjunction with a mini-batch proposal selection mechanism that allows a network to detect both traffic lights and signs from training on separate traffic light and sign datasets. Our method solves the overlapping issue where instances from one dataset are not labelled in the other dataset. We are the first to present a network that performs joint detection on traffic lights and signs. We measure our network on the Tsinghua-Tencent 100K benchmark for traffic sign detection and the Bosch Small Traffic Lights benchmark for traffic light detection and show it outperforms the existing Bosch Small Traffic light state-of-the-art method. We focus on autonomous car deployment and show our network is more suitable than others because of its low memory footprint and real-time image processing time. Qualitative results can be viewed at https://youtu.be/ YmogPzBXOw.
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
(2016)

Leveraging large historical data in electronic health record (EHR), we developed Doctor AI, a generic predictive model that covers observed medical conditions and medication uses. Doctor AI is a temporal model using recurrent neural networks (RNN) and was developed and applied to longitudinal time stamped EHR data from 260K patients over 8 years. Encounter records (e.g. diagnosis codes, medication codes or procedure codes) were input to RNN to predict (all) the diagnosis and medication categories for a subsequent visit. Doctor AI assesses the history of patients to make multilabel predictions (one label for each diagnosis or medication category). Based on separate blind test set evaluation, Doctor AI can perform differential diagnosis with up to 79% recall@30, significantly higher than several baselines. Moreover, we demonstrate great generalizability of Doctor AI by adapting the resulting models from one institution to another without losing substantial accuracy.